Import Library¶
In [44]:
# tensorflow libraries
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Activation, Add, BatchNormalization, Conv2D, Flatten, Dense, DepthwiseConv2D, Dropout, GlobalAveragePooling2D, Input, Layer, MaxPool2D, MaxPooling2D, Multiply, ReLU
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import load_model
from tensorflow.keras.callbacks import Callback, EarlyStopping,ModelCheckpoint, ReduceLROnPlateau
# system libraries
import os
import random
from pathlib import Path
# visualization libraries
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import cv2
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, make_scorer
from sklearn.model_selection import train_test_split
In [2]:
# Define constants
DATASET_PATH = "C:\\Users\\User\\Downloads\\FYP-test\\condition_ds\\"
BATCH_SIZE = 32
EPOCHS = 20
TARGET_SIZE = (224, 224)
Helper function¶
In [3]:
def walk_through_dir(dir_path):
for dirpath, dirnames, filenames in os.walk(dir_path):
print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
In [4]:
def load_dataset(file):
data = []
class_labels = []
for label, class_folder in enumerate(os.listdir(file)):
class_labels.append(class_folder)
class_path = os.path.join(file, class_folder)
if os.path.isdir(class_path):
for image_file in os.listdir(class_path):
image_path = os.path.join(class_path, image_file)
# Append (file_path, label) tuple to data list
data.append((image_path, label))
return data, class_labels
In [5]:
# Walk through each directory for apple conditions
walk_through_dir(DATASET_PATH)
There are 2 directories and 0 images in 'C:\Users\User\Downloads\FYP-test\condition_ds\'. There are 0 directories and 911 images in 'C:\Users\User\Downloads\FYP-test\condition_ds\fresh'. There are 0 directories and 912 images in 'C:\Users\User\Downloads\FYP-test\condition_ds\rotten'.
In [6]:
image, labels = load_dataset(DATASET_PATH)
print(image)
print(labels)
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['fresh', 'rotten']
Data Visualization¶
In [7]:
image_dir = Path(DATASET_PATH)
# Get filepaths and labels
filepaths = list(image_dir.glob(r'**/*.jpg')) + list(image_dir.glob(r'**/*.png'))
condition_labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))
filepaths = pd.Series(filepaths, name='Filepath').astype(str)
condition_labels = pd.Series(condition_labels, name='Label')
# Concatenate filepaths and labels
image_df = pd.concat([filepaths, condition_labels], axis=1)
print(condition_labels)
0 fresh
1 fresh
2 fresh
3 fresh
4 fresh
...
1818 rotten
1819 rotten
1820 rotten
1821 rotten
1822 rotten
Name: Label, Length: 1823, dtype: object
In [8]:
label_counts = image_df['Label'].value_counts()[:20]
plt.figure(figsize=(20, 6))
sns.barplot(x=label_counts.index, y=label_counts.values, alpha=0.8, palette='Blues')
plt.title('Distribution of labels in Apple Condition Dataset ', fontsize=16)
plt.xlabel('Label', fontsize=14)
plt.ylabel('Count', fontsize=14)
plt.xticks(rotation=45)
plt.show()
In [9]:
random_index = np.random.randint(0, len(image_df), 16)
fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(10, 10),
subplot_kw={'xticks': [], 'yticks': []})
for i, ax in enumerate(axes.flat):
ax.imshow(plt.imread(image_df.Filepath[random_index[i]]))
ax.set_title(image_df.Label[random_index[i]])
plt.tight_layout()
plt.show()
Data Preprocessing¶
In [10]:
# Separate in train and test data
train_df, test_df = train_test_split(image_df, test_size=0.2, shuffle=True, random_state=42)
In [11]:
# Set up data generators for training, validation, and testing
train_datagen = ImageDataGenerator(
rescale=1./255, # Rescale pixel values to [0, 1]
validation_split=0.2, # Split 20% of the data for validation
rotation_range=15, # Randomly rotate images by up to 20 degrees
width_shift_range=0.1, # Randomly shift images horizontally by up to 10%
height_shift_range=0.1, # Randomly shift images vertically by up to 10%
shear_range=0.1, # Apply shear transformation with intensity up to 10%
zoom_range=0.1, # Randomly zoom into images by up to 10%
horizontal_flip=True, # Randomly flip images horizontally
fill_mode='nearest' # Strategy for filling in newly created pixels after rotation or shifting
)
# Create a generator for training data
train_generator = train_datagen.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath', # Column containing the file paths
y_col='Label', # Column containing the labels
target_size=TARGET_SIZE,
color_mode='rgb',
batch_size=BATCH_SIZE,
class_mode='binary', # For binary classification
shuffle=True, # Shuffle the data
seed=42,
subset='training'
)
# Create a generator for validation data
validation_generator = train_datagen.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath', # Column containing the file paths
y_col='Label', # Column containing the labels
target_size=TARGET_SIZE,
color_mode='rgb',
batch_size=BATCH_SIZE,
class_mode='binary', # For binary classification
shuffle=True, # Shuffle the data
seed=42,
subset='validation'
)
# Create a generator for testing data (if needed)
test_generator = train_datagen.flow_from_dataframe(
dataframe=test_df,
x_col='Filepath', # Column containing the file paths
y_col='Label', # Column containing the labels
target_size=TARGET_SIZE,
color_mode='rgb',
batch_size=BATCH_SIZE,
class_mode='binary', # For binary classification
shuffle=False # No need to shuffle for testing
)
# Display the class indices
print("Class Indices:", train_generator.class_indices)
# Display the number of samples in each split
print("Number of training samples:", train_generator.samples)
print("Number of validation samples:", validation_generator.samples)
print("Number of testing samples:", test_generator.samples)
Found 1167 validated image filenames belonging to 2 classes.
Found 291 validated image filenames belonging to 2 classes.
Found 365 validated image filenames belonging to 2 classes.
Class Indices: {'fresh': 0, 'rotten': 1}
Number of training samples: 1167
Number of validation samples: 291
Number of testing samples: 365
In [12]:
# Fetch a batch of images and labels from the data generator
x_batch, y_batch = next(train_generator)
# Visualize the first few images and their labels
plt.figure(figsize=(10, 10))
for i in range(min(16, len(x_batch))): # Visualize up to 16 images
plt.subplot(4, 4, i + 1)
plt.imshow(x_batch[i])
plt.title('Label: ' + str(y_batch[i]))
plt.axis('off')
plt.show()
In [13]:
print(y_batch)
[0. 1. 1. 1. 1. 0. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
Define Model¶
Model 1: custom CNN¶
In [116]:
# Define the model
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Conv2D(128, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(256, activation='relu'),
Dropout(0.5),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(2, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Print model summary
model.summary()
Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_9 (Conv2D) (None, 222, 222, 32) 896
max_pooling2d_9 (MaxPoolin (None, 111, 111, 32) 0
g2D)
conv2d_10 (Conv2D) (None, 109, 109, 64) 18496
max_pooling2d_10 (MaxPooli (None, 54, 54, 64) 0
ng2D)
conv2d_11 (Conv2D) (None, 52, 52, 128) 73856
max_pooling2d_11 (MaxPooli (None, 26, 26, 128) 0
ng2D)
flatten_3 (Flatten) (None, 86528) 0
dense_16 (Dense) (None, 256) 22151424
dropout_6 (Dropout) (None, 256) 0
dense_17 (Dense) (None, 128) 32896
dropout_7 (Dropout) (None, 128) 0
dense_18 (Dense) (None, 2) 258
=================================================================
Total params: 22277826 (84.98 MB)
Trainable params: 22277826 (84.98 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
Model 2: MobileNet V2¶
In [123]:
base_model = MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights=None)
# Freeze the base model
base_model.trainable = False
model2_1 = tf.keras.Sequential([
base_model,
tf.keras.layers.GlobalAveragePooling2D(),
# tf.keras.layers.Dense(768, activation='relu'),
# tf.keras.layers.Dense(768, activation='relu'),
tf.keras.layers.Dense(2, activation='softmax') # Adjust the number of units to match the number of classes
])
model2_1.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model2_1.summary()
Model: "sequential_11"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenetv2_1.00_224 (Func (None, 7, 7, 1280) 2257984
tional)
global_average_pooling2d_7 (None, 1280) 0
(GlobalAveragePooling2D)
dense_19 (Dense) (None, 2) 2562
=================================================================
Total params: 2260546 (8.62 MB)
Trainable params: 2562 (10.01 KB)
Non-trainable params: 2257984 (8.61 MB)
_________________________________________________________________
Model 3: Pre-trained MobileNetV2¶
In [103]:
# Define the MobileNetV2 model with pre-trained weights from ImageNet
pretrained_mobilenetv2 = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights='imagenet')
# Freeze the weights of the pre-trained layers
pretrained_mobilenetv2.trainable = False
# Add a custom classification head on top of the pre-trained model
model2_2 = tf.keras.models.Sequential([
pretrained_mobilenetv2,
tf.keras.layers.GlobalAveragePooling2D(),
# tf.keras.layers.Dense(768, activation='relu'),
# tf.keras.layers.Dense(768, activation='relu'),
tf.keras.layers.Dense(2, activation='softmax') # Adjusted number of units
])
# Compile the model
model2_2.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Display the model summary
model2_2.summary()
Model: "sequential_8"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenetv2_1.00_224 (Func (None, 7, 7, 1280) 2257984
tional)
global_average_pooling2d_5 (None, 1280) 0
(GlobalAveragePooling2D)
dense_14 (Dense) (None, 2) 2562
=================================================================
Total params: 2260546 (8.62 MB)
Trainable params: 2562 (10.01 KB)
Non-trainable params: 2257984 (8.61 MB)
_________________________________________________________________
Model Training¶
Model 1: Custom CNN¶
In [117]:
callbacks = [
EarlyStopping(patience=5, monitor='val_loss', verbose=1),
ModelCheckpoint('custom_CNN_condition.h5', save_best_only=True, monitor='val_loss', mode='min'),
ReduceLROnPlateau(factor=0.1, patience=2, min_lr=0.00001, verbose=1),
]
# Train the model
history = model.fit(
train_generator,
steps_per_epoch= train_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
validation_data=validation_generator,
validation_steps=validation_generator.samples // BATCH_SIZE,
callbacks=callbacks
)
Epoch 1/20 36/36 [==============================] - ETA: 0s - loss: 0.9643 - accuracy: 0.5225
C:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
36/36 [==============================] - 40s 1s/step - loss: 0.9643 - accuracy: 0.5225 - val_loss: 0.6910 - val_accuracy: 0.4722 - lr: 0.0010 Epoch 2/20 36/36 [==============================] - 34s 949ms/step - loss: 0.6334 - accuracy: 0.6388 - val_loss: 0.5801 - val_accuracy: 0.7500 - lr: 0.0010 Epoch 3/20 36/36 [==============================] - 54s 1s/step - loss: 0.4900 - accuracy: 0.7868 - val_loss: 0.3828 - val_accuracy: 0.8403 - lr: 0.0010 Epoch 4/20 36/36 [==============================] - 35s 963ms/step - loss: 0.3969 - accuracy: 0.8203 - val_loss: 0.3087 - val_accuracy: 0.8715 - lr: 0.0010 Epoch 5/20 36/36 [==============================] - 33s 906ms/step - loss: 0.4131 - accuracy: 0.8238 - val_loss: 0.2461 - val_accuracy: 0.8993 - lr: 0.0010 Epoch 6/20 36/36 [==============================] - 31s 873ms/step - loss: 0.3375 - accuracy: 0.8502 - val_loss: 0.2466 - val_accuracy: 0.8785 - lr: 0.0010 Epoch 7/20 36/36 [==============================] - ETA: 0s - loss: 0.3215 - accuracy: 0.8687 Epoch 7: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513. 36/36 [==============================] - 31s 865ms/step - loss: 0.3215 - accuracy: 0.8687 - val_loss: 0.2645 - val_accuracy: 0.8819 - lr: 0.0010 Epoch 8/20 36/36 [==============================] - 32s 902ms/step - loss: 0.2707 - accuracy: 0.8943 - val_loss: 0.2067 - val_accuracy: 0.9028 - lr: 1.0000e-04 Epoch 9/20 36/36 [==============================] - 34s 953ms/step - loss: 0.2543 - accuracy: 0.8943 - val_loss: 0.2162 - val_accuracy: 0.9028 - lr: 1.0000e-04 Epoch 10/20 36/36 [==============================] - 34s 931ms/step - loss: 0.2387 - accuracy: 0.9022 - val_loss: 0.1923 - val_accuracy: 0.8958 - lr: 1.0000e-04 Epoch 11/20 36/36 [==============================] - 34s 932ms/step - loss: 0.2325 - accuracy: 0.9089 - val_loss: 0.1859 - val_accuracy: 0.9028 - lr: 1.0000e-04 Epoch 12/20 36/36 [==============================] - 33s 921ms/step - loss: 0.2391 - accuracy: 0.9013 - val_loss: 0.1635 - val_accuracy: 0.9236 - lr: 1.0000e-04 Epoch 13/20 36/36 [==============================] - 33s 919ms/step - loss: 0.2130 - accuracy: 0.9172 - val_loss: 0.1904 - val_accuracy: 0.9097 - lr: 1.0000e-04 Epoch 14/20 36/36 [==============================] - ETA: 0s - loss: 0.2239 - accuracy: 0.9198 Epoch 14: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05. 36/36 [==============================] - 33s 919ms/step - loss: 0.2239 - accuracy: 0.9198 - val_loss: 0.1808 - val_accuracy: 0.9097 - lr: 1.0000e-04 Epoch 15/20 36/36 [==============================] - 34s 938ms/step - loss: 0.2268 - accuracy: 0.9048 - val_loss: 0.1732 - val_accuracy: 0.9236 - lr: 1.0000e-05 Epoch 16/20 36/36 [==============================] - ETA: 0s - loss: 0.2182 - accuracy: 0.9154 Epoch 16: ReduceLROnPlateau reducing learning rate to 1e-05. 36/36 [==============================] - 33s 929ms/step - loss: 0.2182 - accuracy: 0.9154 - val_loss: 0.1924 - val_accuracy: 0.9062 - lr: 1.0000e-05 Epoch 17/20 36/36 [==============================] - 33s 915ms/step - loss: 0.2248 - accuracy: 0.9048 - val_loss: 0.1874 - val_accuracy: 0.9271 - lr: 1.0000e-05 Epoch 17: early stopping
Model 2: MobileNet V2¶
In [124]:
callbacks = [
EarlyStopping(patience=5, monitor='val_loss', verbose=1),
ModelCheckpoint('MobileNetV2_condition2_1.h5', save_best_only=True, monitor='val_loss', mode='min')
# ReduceLROnPlateau(factor=0.1, patience=2, min_lr=0.00001, verbose=1),
]
# Train the model
history = model2_1.fit(
train_generator,
steps_per_epoch=train_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
validation_data=validation_generator,
validation_steps=validation_generator.samples // BATCH_SIZE,
callbacks=callbacks
)
Epoch 1/20 36/36 [==============================] - 25s 623ms/step - loss: 0.6932 - accuracy: 0.4960 - val_loss: 0.6933 - val_accuracy: 0.4722 Epoch 2/20
C:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
36/36 [==============================] - 22s 605ms/step - loss: 0.6931 - accuracy: 0.5113 - val_loss: 0.6936 - val_accuracy: 0.4757 Epoch 3/20 36/36 [==============================] - 22s 613ms/step - loss: 0.6931 - accuracy: 0.5145 - val_loss: 0.6942 - val_accuracy: 0.4653 Epoch 4/20 36/36 [==============================] - 22s 596ms/step - loss: 0.6929 - accuracy: 0.5137 - val_loss: 0.6943 - val_accuracy: 0.4688 Epoch 5/20 36/36 [==============================] - 22s 606ms/step - loss: 0.6930 - accuracy: 0.5101 - val_loss: 0.6944 - val_accuracy: 0.4688 Epoch 6/20 36/36 [==============================] - 23s 650ms/step - loss: 0.6930 - accuracy: 0.5093 - val_loss: 0.6944 - val_accuracy: 0.4688 Epoch 6: early stopping
Model 3: Pre-trained MobileNet V2¶
In [104]:
callbacks = [
EarlyStopping(patience=5, monitor='val_loss', verbose=1),
ModelCheckpoint('MobileNetV2_condition2_69.h5', save_best_only=True, monitor='val_loss', mode='min')
# ReduceLROnPlateau(factor=0.1, patience=2, min_lr=0.00001, verbose=1),
]
# Train the model
history = model2_2.fit(
train_generator,
steps_per_epoch=train_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
validation_data=validation_generator,
validation_steps=validation_generator.samples // BATCH_SIZE,
callbacks=callbacks
)
Epoch 1/20 36/36 [==============================] - 30s 795ms/step - loss: 0.4217 - accuracy: 0.8009 - val_loss: 0.2350 - val_accuracy: 0.9097 Epoch 2/20 36/36 [==============================] - 26s 734ms/step - loss: 0.1896 - accuracy: 0.9374 - val_loss: 0.1593 - val_accuracy: 0.9479 Epoch 3/20 36/36 [==============================] - 26s 711ms/step - loss: 0.1311 - accuracy: 0.9674 - val_loss: 0.1459 - val_accuracy: 0.9514 Epoch 4/20 36/36 [==============================] - 25s 704ms/step - loss: 0.1106 - accuracy: 0.9736 - val_loss: 0.1222 - val_accuracy: 0.9549 Epoch 5/20 36/36 [==============================] - 25s 693ms/step - loss: 0.0966 - accuracy: 0.9727 - val_loss: 0.1130 - val_accuracy: 0.9653 Epoch 6/20 36/36 [==============================] - 25s 690ms/step - loss: 0.0815 - accuracy: 0.9789 - val_loss: 0.0921 - val_accuracy: 0.9722 Epoch 7/20 36/36 [==============================] - 25s 690ms/step - loss: 0.0700 - accuracy: 0.9815 - val_loss: 0.1109 - val_accuracy: 0.9479 Epoch 8/20 36/36 [==============================] - 25s 701ms/step - loss: 0.0651 - accuracy: 0.9806 - val_loss: 0.1166 - val_accuracy: 0.9618 Epoch 9/20 36/36 [==============================] - 25s 698ms/step - loss: 0.0607 - accuracy: 0.9789 - val_loss: 0.0758 - val_accuracy: 0.9688 Epoch 10/20 36/36 [==============================] - 25s 698ms/step - loss: 0.0532 - accuracy: 0.9894 - val_loss: 0.0671 - val_accuracy: 0.9861 Epoch 11/20 36/36 [==============================] - 25s 707ms/step - loss: 0.0524 - accuracy: 0.9868 - val_loss: 0.0971 - val_accuracy: 0.9583 Epoch 12/20 36/36 [==============================] - 27s 746ms/step - loss: 0.0414 - accuracy: 0.9930 - val_loss: 0.0688 - val_accuracy: 0.9757 Epoch 13/20 36/36 [==============================] - 27s 759ms/step - loss: 0.0409 - accuracy: 0.9903 - val_loss: 0.0784 - val_accuracy: 0.9757 Epoch 14/20 36/36 [==============================] - 29s 800ms/step - loss: 0.0388 - accuracy: 0.9938 - val_loss: 0.0701 - val_accuracy: 0.9792 Epoch 15/20 36/36 [==============================] - 29s 820ms/step - loss: 0.0381 - accuracy: 0.9885 - val_loss: 0.0644 - val_accuracy: 0.9757 Epoch 16/20 36/36 [==============================] - 29s 820ms/step - loss: 0.0328 - accuracy: 0.9912 - val_loss: 0.0625 - val_accuracy: 0.9826 Epoch 17/20 36/36 [==============================] - 27s 751ms/step - loss: 0.0351 - accuracy: 0.9903 - val_loss: 0.0887 - val_accuracy: 0.9653 Epoch 18/20 36/36 [==============================] - 28s 780ms/step - loss: 0.0322 - accuracy: 0.9930 - val_loss: 0.0627 - val_accuracy: 0.9688 Epoch 19/20 36/36 [==============================] - 28s 768ms/step - loss: 0.0278 - accuracy: 0.9956 - val_loss: 0.0625 - val_accuracy: 0.9792 Epoch 20/20 36/36 [==============================] - 28s 774ms/step - loss: 0.0334 - accuracy: 0.9930 - val_loss: 0.0587 - val_accuracy: 0.9757
Model Evaluation¶
Model 1: Custom CNN¶
In [118]:
# Evaluate the trained model on the test set (run this when already done training)
model1 = load_model('custom_CNN_condition.h5')
test_loss, test_accuracy = model1.evaluate(test_generator)
# # Evaluate the model on the test set
# test_loss, test_accuracy = model.evaluate(test_generator)
# Print the test loss and accuracy
print("Test Loss:", test_loss)
print("Test Accuracy:", test_accuracy)
12/12 [==============================] - 5s 401ms/step - loss: 0.1953 - accuracy: 0.9288 Test Loss: 0.19526790082454681 Test Accuracy: 0.9287671446800232
In [119]:
# Extracting training and validation accuracy and loss
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
# Calculating the number of epochs
epochs = range(1, len(accuracy) + 1)
# Plotting accuracy
plt.plot(epochs, accuracy, 'b', label='Training accuracy')
plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
# Plotting loss
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()
Model 2: MobileNet V2¶
In [125]:
# Evaluate the trained model on the test set (run this when already done training)
model2 = load_model('MobileNetV2_condition2_1.h5')
test_loss, test_accuracy = model2.evaluate(test_generator)
# # Evaluate the model on the test set
# test_loss, test_accuracy = model2_1.evaluate(test_generator)
# Print the test loss and accuracy
print("Test Loss:", test_loss)
print("Test Accuracy:", test_accuracy)
12/12 [==============================] - 6s 467ms/step - loss: 0.6932 - accuracy: 0.4849 Test Loss: 0.6932156085968018 Test Accuracy: 0.48493149876594543
In [126]:
# Extracting training and validation accuracy and loss (model2_1)
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
# Calculating the number of epochs
epochs = range(1, len(accuracy) + 1)
# Plotting accuracy
plt.plot(epochs, accuracy, 'b', label='Training accuracy')
plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
# Plotting loss
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()
Model 3: Pre-trained MobileNet V2¶
In [111]:
# Evaluate the trained model on the test set (run this when already done training)
model3 = load_model('MobileNetV2_condition2_2.h5')
test_loss, test_accuracy = model3.evaluate(test_generator)
# # Evaluate the model on the test set
# test_loss, test_accuracy = model2_2.evaluate(test_generator)
# Print the test loss and accuracy
print("Test Loss:", test_loss)
print("Test Accuracy:", test_accuracy)
12/12 [==============================] - 6s 446ms/step - loss: 0.0828 - accuracy: 0.9644 Test Loss: 0.0828077644109726 Test Accuracy: 0.9643835425376892
In [106]:
# Extracting training and validation accuracy and loss (model2_2)
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
# Calculating the number of epochs
epochs = range(1, len(accuracy) + 1)
# Plotting accuracy
plt.plot(epochs, accuracy, 'b', label='Training accuracy')
plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
# Plotting loss
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()
Model Testing¶
Model 1: Custom CNN¶
In [120]:
# Predict the label of the test_images
probs = model1.predict(test_generator)
pred = np.argmax(probs,axis=1)
# Map the label
labels = (train_generator.class_indices)
labels = dict((v,k) for k,v in labels.items()) # {0: 'fresh', 1: 'rotten'}
pred_labels = [labels[k] for k in pred]
12/12 [==============================] - 5s 368ms/step
In [121]:
# Display 25 random pictures from the dataset with their labels
random_index = np.random.randint(0, len(test_df) - 1, 15)
fig, axes = plt.subplots(nrows=3, ncols=5, figsize=(25, 15),
subplot_kw={'xticks': [], 'yticks': []})
for i, ax in enumerate(axes.flat):
ax.imshow(plt.imread(test_df.Filepath.iloc[random_index[i]]))
if test_df.Label.iloc[random_index[i]] == pred_labels[random_index[i]]:
color = "green"
else:
color = "red"
ax.set_title(f"Actual: {test_df.Label.iloc[random_index[i]]}\nPredicted: {pred_labels[random_index[i]]}", color=color)
plt.show()
plt.tight_layout()
<Figure size 640x480 with 0 Axes>
In [122]:
# Get true labels from the test generator
actual = test_generator.classes
# Plot confusion matrix
cm = confusion_matrix(actual, pred)
plt.figure(figsize=(8, 8))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix (Custom CNN)')
plt.show()
# Print classification report
class_labels = list(test_generator.class_indices.keys())
print(classification_report(actual, pred, target_names=class_labels))
precision recall f1-score support
fresh 0.92 0.94 0.93 177
rotten 0.94 0.92 0.93 188
accuracy 0.93 365
macro avg 0.93 0.93 0.93 365
weighted avg 0.93 0.93 0.93 365
Model 2: MobileNet V2¶
In [127]:
# Predict the label of the test_images
probs2 = model2.predict(test_generator)
pred2 = np.argmax(probs2,axis=1)
# Map the label
labels2 = (train_generator.class_indices)
labels2 = dict((v,k) for k,v in labels2.items()) # {0: 'fresh', 1: 'rotten'}
pred_labels2 = [labels2[k] for k in pred2]
12/12 [==============================] - 6s 445ms/step
In [128]:
# Display 25 random pictures from the dataset with their labels
random_index = np.random.randint(0, len(test_df) - 1, 15)
fig, axes = plt.subplots(nrows=3, ncols=5, figsize=(25, 15),
subplot_kw={'xticks': [], 'yticks': []})
for i, ax in enumerate(axes.flat):
ax.imshow(plt.imread(test_df.Filepath.iloc[random_index[i]]))
if test_df.Label.iloc[random_index[i]] == pred_labels2[random_index[i]]:
color = "green"
else:
color = "red"
ax.set_title(f"Actual: {test_df.Label.iloc[random_index[i]]}\nPredicted: {pred_labels2[random_index[i]]}", color=color)
plt.show()
plt.tight_layout()
<Figure size 640x480 with 0 Axes>
In [129]:
# Get true labels from the test generator
actual = test_generator.classes
# Plot confusion matrix
cm = confusion_matrix(actual, pred2)
plt.figure(figsize=(8, 8))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix (MobileNet V2)')
plt.show()
# Print classification report
class_labels = list(test_generator.class_indices.keys())
print(classification_report(actual, pred2, target_names=class_labels))
precision recall f1-score support
fresh 0.48 1.00 0.65 177
rotten 0.00 0.00 0.00 188
accuracy 0.48 365
macro avg 0.24 0.50 0.33 365
weighted avg 0.24 0.48 0.32 365
C:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) C:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) C:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
Model 3: Pre-trained MobileNet V2¶
In [112]:
# Predict the label of the test_images
probs3 = model3.predict(test_generator)
pred3 = np.argmax(probs3,axis=1)
# Map the label
labels3 = (train_generator.class_indices)
labels3 = dict((v,k) for k,v in labels3.items()) # {0: 'fresh', 1: 'rotten'}
pred_labels3 = [labels3[k] for k in pred3]
12/12 [==============================] - 6s 454ms/step
In [113]:
# Display 25 random pictures from the dataset with their labels
random_index = np.random.randint(0, len(test_df) - 1, 15)
fig, axes = plt.subplots(nrows=3, ncols=5, figsize=(25, 15),
subplot_kw={'xticks': [], 'yticks': []})
for i, ax in enumerate(axes.flat):
ax.imshow(plt.imread(test_df.Filepath.iloc[random_index[i]]))
if test_df.Label.iloc[random_index[i]] == pred_labels3[random_index[i]]:
color = "green"
else:
color = "red"
ax.set_title(f"Actual: {test_df.Label.iloc[random_index[i]]}\nPredicted: {pred_labels3[random_index[i]]}", color=color)
plt.show()
plt.tight_layout()
<Figure size 640x480 with 0 Axes>
In [114]:
# Get true labels from the test generator
actual = test_generator.classes
# Plot confusion matrix
cm = confusion_matrix(actual, pred3)
plt.figure(figsize=(8, 8))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix (Pre-trained MobileNet V2)')
plt.show()
# Print classification report
class_labels = list(test_generator.class_indices.keys())
print(classification_report(actual, pred3, target_names=class_labels))
precision recall f1-score support
fresh 0.96 0.99 0.97 177
rotten 0.99 0.96 0.98 188
accuracy 0.98 365
macro avg 0.98 0.98 0.98 365
weighted avg 0.98 0.98 0.98 365
Real world testing¶
In [130]:
# Load and preprocess the image
img_path = 'testing/t1.jpg'
img = image.load_img(img_path, target_size=(224, 224)) # Adjust target_size as needed
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array /= 255. # Normalize pixel values
# Predict the label
probs = model1.predict(img_array)
pred_label_idx = np.argmax(probs)
pred_label = labels[pred_label_idx]
# Display the image and predicted label
plt.imshow(img)
plt.title(f'Predicted Label: {pred_label}')
plt.axis('off')
plt.show()
1/1 [==============================] - 0s 68ms/step
In [131]:
# Load and preprocess the image
img_path = 'testing/t1.jpg'
img = image.load_img(img_path, target_size=(224, 224)) # Adjust target_size as needed
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array /= 255. # Normalize pixel values
# Predict the label
probs = model2.predict(img_array)
pred_label_idx = np.argmax(probs)
pred_label = labels[pred_label_idx]
# Display the image and predicted label
plt.imshow(img)
plt.title(f'Predicted Label: {pred_label}')
plt.axis('off')
plt.show()
1/1 [==============================] - 0s 481ms/step
In [110]:
# Load and preprocess the image
img_path = 'testing/t2.jpg'
img = image.load_img(img_path, target_size=(224, 224)) # Adjust target_size as needed
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array /= 255. # Normalize pixel values
# Predict the label
probs = model3.predict(img_array)
pred_label_idx = np.argmax(probs)
pred_label = labels[pred_label_idx]
# Display the image and predicted label
plt.imshow(img)
plt.title(f'Predicted Label: {pred_label}')
plt.axis('off')
plt.show()
1/1 [==============================] - 1s 610ms/step
